What is predictive analytics?

Predictive analytics is a branch of data analytics that uses an existing data set to estimate future outcomes. The fundamental concept is that by analyzing past and present data in a given context, it’s possible to identify certain patterns. These patterns can, if interpreted correctly, inform accurate predictions of future events.

So where does machine learning factor into this? Well, technically, it’s perfectly possible to perform a form of predictive analytics without computers at all. You can, and probably often do, make predictions with a simple pencil and paper, or even just in your head.

Take an example of an independent store owner who wants to work out his most efficient opening hours. By looking at receipts of the store’s takings over the last few weeks, the owner could work out the revenue for each hour of the day. With this information, he could match it against running costs and fairly accurately predict what times to close up in the future to save money, without even needing a computer.

This is a perfect example of predictive analytics, however, it’s also a very basic one. Problems start arising when you want to scale the predictions and make them as accurate as possible. For instance, financial institutions and governments may be dealing with millions or even billions of dollars and enormous data sets. In cases like these, the power of computers and machine learning becomes a lot more relevant, not to mention more economical.

Why is predictive analytics important?

Over time, the field of predictive analytics has become incredibly important for businesses, industry, governments, and consumers.

But until computers became widespread, predictive analytics as we know it today was non-existent. Technology lagged behind the demand for high-quality predictive data. This resulted in organizations needing to outlay massive amounts of time and money to adapt to change rather than foresee it.

In the mid 20th Century, the first governments and researchers started to make use of non-linear programming to create adaptive computational models. This pioneering work was so resource-intensive that it was limited to large government agencies.

“Leaps forward have facilitated the growth of machine learning into an extremely reliable tool for organizations to access powerful predictive insights into future possibilities.”

Since then, things have changed dramatically. Within just the last few decades there have been exponential advancements in computing power and other relevant technologies. These leaps forward have facilitated the growth of machine learning into an extremely reliable tool for organizations to access powerful predictive insights into future possibilities.

And the potential of being able to predict the future with accuracy is enormous. Far from being limited to governments, small and medium-sized enterprises (SMEs) throughout the world are rapidly catching on and implementing predictive analytics into their operating strategies. Health organizations use predictive analytics powered by machine learning to prepare for surges in demand. Governments use it to prevent crime. Businesses use it to plan production levels ahead of supply or demand fluctuations.

Machine learning and predictive analytics are subtly but undoubtedly helping to shape the world around us, and this is only set to continue as technology advances.

Use cases

Nowadays, you don’t have to be a government or a tech giant to benefit from machine learning. SMEs across a range of industries can now harness the power of predictive analytics.

Inventory Optimization

Most businesses in the world rely on some form of inventory analysis, balancing the need for stock levels to be sufficient to meet upcoming demand whilst not ending up with excess stock. Accurate inventory management is of particular importance to companies dealing with time-sensitive or perishable goods, such as food importers. Where these companies may have traditionally used spreadsheets to foresee demand spikes, they are increasingly utilizing predictive analytics to improve the accuracy of their inventory management, making significant savings as a result.

Industrial Efficiency

Predictive analytics is arguably having the biggest impact on industrial processes. Factories and R&D departments across the globe are being revolutionized by AI-powered advancements, many of which involve the prediction of future outcomes. One prominent example is the field predictive maintenance. This involves modeling when repairs will be needed for industrial equipment and infrastructure. With machine learning-based predictive analytics, the process of predictive maintenance can be made far more accurate. This creates huge cost-reduction opportunities for industries such as Oil & Gas, where it can be incredibly expensive to unexpectedly deploy repair teams to deep-water platforms.

Workforce Analytics

Human resources departments are the primary users of these kinds of predictions. In this context, workforce data are collected and input into machine learning frameworks that can predict things like employee churn rates. It’s easy to see the potential of this sort of technology to optimize working environments, improve employee satisfaction, and increase profit margins.

Don’t get left behind

Predictive analytics has the power to transform your business.

Sciling is a team of AI experts with decades of collective academic and professional experience in the field of machine learning. We not only build commercial predictive analytical models, but we also helped to formulate and research many of the concepts behind them. This gives us a unique ability to tailor our services perfectly to any application.

Want to learn how Sciling can create custom technology to solve your business challenges?

Talk to us.

Get in touch with the team to see how we can help you.